summary.fixest {fixest} | R Documentation

## Summary of a `fixest` object. Computes different types of standard errors.

### Description

This function is similar to `print.fixest`. It provides the table of coefficients along with other information on the fit of the estimation. It can compute different types of standard errors. The new variance covariance matrix is an object returned.

### Usage

    ## S3 method for class 'fixest'
    summary(object, se, cluster, dof = TRUE,
      exact_dof = FALSE, forceCovariance = FALSE, keepBounded = FALSE,
      ...)

### Arguments

`object` | A `fixest` object. Obtained using the functions `femlm`, `feols` or `feglm`.
`se` | Character scalar. Which kind of standard error should be computed: “standard”, “White”, “cluster”, “twoway”, “threeway” or “fourway”? By default if there are clusters in the estimation: `se = "cluster"`, otherwise `se = "standard"`. Note that this argument can be implicitly deduced from the argument `cluster`.

`cluster`

Tells how to cluster the standard-errors (if clustering is requested). Can be either a list of vectors, a character vector of variable names, a formula or an integer vector. Assume we want to perform 2-way clustering over `var1` and `var2` contained in the data.frame `base` used for the estimation. All the following `cluster` arguments are valid and do the same thing: `cluster = base[, c("var1, "var2")]}, \code{cluster = c("var1, "var2")`, `cluster = ~var1+var2`. If the two variables were used as clusters in the estimation, you could further use `cluster = 1:2` or leave it blank with `se = "twoway"` (assuming `var1` [resp. `var2`] was the 1st [res. 2nd] cluster).

`dof` | Logical, default is `TRUE`. Should there be a degree of freedom correction to the standard errors of the coefficients?
`exact_dof` | Logical, default is `FALSE`. In case there were 2+ clusters in the estimation, it computes the exact number of degrees of freedom (this is not needed in case of balanced panels).
`forceCovariance` | (Advanced users.) Logical, default is `FALSE`. In the peculiar case where the obtained Hessian is not invertible (usually because of collinearity of some variables), use this option to force the covariance matrix, by using a generalized inverse of the Hessian. This can be useful to spot where possible problems come from.
`keepBounded` | (Advanced users – feNmlm with non-linear part and bounded coefficients only.) Logical, default is `FALSE`. If `TRUE`, then the bounded coefficients (if any) are treated as unrestricted coefficients and their S.E. is computed (otherwise it is not).
`...` | Not currently used.

### Value

It returns a `fixest` object with:

`cov.scaled` | The new variance-covariance matrix (computed according to the argument `se`).
`se` | The new standard-errors (computed according to the argument `se`).
`coeftable` | The table of coefficients with the new standard errors.

### Author(s)

Laurent Berge

### See Also

See also the main estimation functions `femlm`, `feols` or `feglm`. Use `fixef.fixest` to extract the cluster coefficients, and the functions `esttable` and `esttex` to visualize the results of multiple estimations.

### Examples

    # Load trade data
    data(trade)

    # We estimate the effect of distance on trade (with 3 cluster effects)
    est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade)

    # Comparing different types of standard errors
    sum_white    = summary(est_pois, se = "white")
    sum_oneway   = summary(est_pois, se = "cluster")
    sum_twoway   = summary(est_pois, se = "twoway")
    sum_threeway = summary(est_pois, se = "threeway")

    esttable(sum_white, sum_oneway, sum_twoway, sum_threeway)

    # Alternative ways to cluster the SE:

    # two-way clustering: Destination and Product
    # (Note that arg. se = "twoway" is implicitly deduced from the argument cluster)
    summary(est_pois, cluster = c("Destination", "Product"))
    summary(est_pois, cluster = trade[, c("Destination", "Product")])
    summary(est_pois, cluster = list(trade$Destination, trade$Product))
    summary(est_pois, cluster = ~Destination+Product)
    # Since Destination and Product are used as fixed-effects, you can also use:
    summary(est_pois, cluster = 2:3)

[Package fixest version 0.1.0 Index]